In today’s digital era, the realms of virtual reality (VR), augmented reality (AR), and mixed reality (MR) collectively referred to as extended reality (XR) are reshaping human–computer interactions. XR technologies are poised to overcome geographical barriers, offering innovative solutions for enhancing emotional and social engagement in telecommunications and remote collaboration. This paper delves into the integration of (AI)-powered 3D talking heads within XR-based telecommunication systems. These avatars replicate human expressions, gestures, and speech, effectively minimizing physical constraints in remote communication. The contributions of this research encompass an extensive examination of audio-driven 3D head generation methods and the establishment of comprehensive evaluation criteria for 3D talking head algorithms within Shared Virtual Environments (SVEs). As XR technology evolves, AI-driven 3D talking heads promise to revolutionize remote collaboration and communication.

Emotion recognition from facial expressions is a challenging task due to the subtle and nuanced nature of facial expressions. Within the framework of Tactile Internet (TI), the integration of this technology has the capacity to completely transform real-time user interactions, by delivering customized emotional input. The influence of this technology is far-reaching, as it may be used in immersive virtual reality interactions and remote tele-care applications to identify emotional states in patients. In this paper, a novel emotion recognition algorithm is presented that integrates a Self-Attention (SA) module into the SlowR50 backbone (SlowR50-SA). The experiments on the DFEW and FERV39K datasets demonstrate that the proposed model achieves good performance in terms of both Unweighted Average Recall (UAR) and Weighted Average Recall (WAR) metrics, achieving a UAR (WAR) of 57.09% (69.87%) on the DFEW dataset, and UAR (WAR) of 39.48% (49.34%) on the FERV39K dataset. Notably, SlowR50-SA operates with only eight frames of input at low temporal resolution, highlighting its efficiency. Furthermore, the algorithm has the potential to be integrated into Tactile Internet applications, where it can be used to enhance the user experience by providing real-time emotion feedback. SlowR50-SA can also be used to enhance virtual reality experiences by providing personalized haptic feedback based on the user’s emotional state. It can also be used in remote tele-care applications to detect signs of stress, anxiety, or depression in patients.

In this paper, we propose a novel feature extraction algorithm based on curvature analysis over the 3D data and the grayscale information extracted from the images. The performance of the method is tested on 3D mesh data, provided by Mars Orbiter Laser Altimeter (MOLA) and compared to benchmark research work. The experimental results demonstrate that the proposed method can achieve better accuracy in comparison with other crater detection methods and have smaller computational complexity.

The integration of material attribute estimation (MAE) within augmented reality, virtual reality, and mixed reality telecommunication systems stands as a pivotal domain, evolving rapidly with the advent of the Tactile Internet. This unifying implementation process has the potential for improvements in the realism and interactivity of immersive environments. The interaction between MAE and the haptic Internet could lead to significant advances in haptic feedback systems, enabling more accurate and responsive user experiences. This systematic review is focused on the intersection of MAE and the Tactile Internet, aiming to find an implementation path between these technologies. Motivated by the potential of the haptic Internet to advance telecommunications, we explore its potential to advance the analysis of material attributes within AR, VR, and MR applications. Through an extensive analysis of current research approaches, including machine learning methods, we explore the possibilities of integrating the TI into MAE. By exploiting haptic and visual properties stored in the materials of 3D objects and using them directly during rendering in remote access scenarios, we propose a conceptual framework that combines data capture, visual representation, processing, and communication in virtual environments.

MATEC Web of Conferences, open access proceedings in Materials science, Engineering and Chemistry